scholarly journals Model-based and data-driven model-reference control: A comparative analysis

2013 ◽  
Author(s):  
Simone Formentin ◽  
Klaske van Heusden ◽  
Alireza Karimi
Keyword(s):  
Comparative Analysis ◽  
Data Driven ◽  
Model Reference ◽  
Model Based ◽  
Model Reference Control

Data-driven model reference control design by prediction error identification

2017 ◽  
Vol 354 (6) ◽  
pp. 2628-2647 ◽  
Author(s):  
Lucíola Campestrini ◽  
Diego Eckhard ◽  
Alexandre Sanfelice Bazanella ◽  
Michel Gevers
Keyword(s):  
Prediction Error ◽  
Control Design ◽  
Data Driven ◽  
Error Identification ◽  
Model Reference ◽  
Model Reference Control

scholarly journals Proper closed-loop specifications for data-driven model-reference control

2021 ◽  
Vol 54 (9) ◽  
pp. 46-51
Author(s):  
Valentina Breschi ◽  
Simone Formentin
Keyword(s):  
Closed Loop ◽  
Data Driven ◽  
Model Reference ◽  
Model Reference Control

Data-driven model reference control of MIMO vertical tank systems with model-free VRFT and Q-Learning

2018 ◽  
Vol 73 ◽  
pp. 227-238 ◽  
Author(s):  
Mircea-Bogdan Radac ◽  
Radu-Emil Precup ◽  
Raul-Cristian Roman
Keyword(s):  
Data Driven ◽  
Model Reference ◽  
Q Learning ◽  
Model Free ◽  
Model Reference Control

Iterative Data-Driven Fractional Model Reference Control of Industrial Robot for Repetitive Precise Speed Tracking

2019 ◽  
Vol 24 (3) ◽  
pp. 1041-1053 ◽  
Author(s):  
Yuanlong Xie ◽  
Xiaoqi Tang ◽  
Wei Meng ◽  
Bosheng Ye ◽  
Bao Song ◽  
...  
Keyword(s):  
Industrial Robot ◽  
Data Driven ◽  
Model Reference ◽  
Fractional Model ◽  
Speed Tracking ◽  
Model Reference Control

Optimal Model Reference Control Scheme Design for Nonlinear Strict-Feedback Systems

2020 ◽  
Vol 38 (9A) ◽  
pp. 1342-1351
Author(s):  
Musadaq A. Hadi ◽  
Hazem I. Ali
Keyword(s):  
Reference Model ◽  
Feedback System ◽  
Optimization Method ◽  
Feedback Systems ◽  
Model Reference ◽  
Scheme Design ◽  
Model Reference Control ◽  
Control Scheme ◽  
Lyapunov Stability Analysis ◽  
Strict Feedback

In this paper, a new design of the model reference control scheme is proposed in a class of nonlinear strict-feedback system. First, the system is analyzed using Lyapunov stability analysis. Next, a model reference is used to improve system performance. Then, the Integral Square Error (ISE) is considered as a cost function to drive the error between the reference model and the system to zero. After that, a powerful metaheuristic optimization method is used to optimize the parameters of the proposed controller. Finally, the results show that the proposed controller can effectively compensate for the strictly-feedback nonlinear system with more desirable performance.

scholarly journals The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2085
Author(s):  
Xue-Bo Jin ◽  
Ruben Jonhson Robert RobertJeremiah ◽  
Ting-Li Su ◽  
Yu-Ting Bai ◽  
Jian-Lei Kong
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Future Development ◽  
Data Driven ◽  
Estimation Methods ◽  
Multiple Model ◽  
Development Trends ◽  
Research Results ◽  
Model Driven ◽  
Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

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